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An adaptive multiobjective evolutionary algorithm based on grid subspaces

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Abstract

The successful application of multi-objective evolutionary algorithms (MOEAs) in many kinds of multiobjective problems have attracted considerable attention in recent years. In this paper, an adaptive multi-objective evolutionary algorithm is proposed by incorporating the concepts of the grid system (denoted as AGMOEA). Based on grid, the objective space is divided into subspaces. Based on the quality and dominance relationship between subspaces, the evolutionary opportunities are dynamically allocated to different subspaces with an adaptive selection strategy. To improve the evolutionary efficiency, the evolutionary scheme and an external archive mechanism considering representative individuals are proposed. The experimental results on 21 benchmark problems demonstrate that the proposed algorithm is competitive or superior to the rival algorithms.

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Acknowledgements

This research was supported by the National Key Research and Development Program of China (2018YFB1700404), the Fund for the National Natural Science Foundation of China (62073067), the Major Program of National Natural Science Foundation of China (71790614), the Major International Joint Research Project of the National Natural Science Foundation of China (71520107004), and the 111 Project (B16009), and the Fundamental Research Funds for the Central Uni-versities (N2128001).

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Authors and Affiliations

  1. The Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang, 110819, China

    Linlin Li

  2. The Liaoning Engineering Laboratory of Operation Analytics and Optimization for Smart Industry, Liaoning Key Laboratory of Manufacturing System and Logistics, Shenyang, 110819, China

    Xianpeng Wang

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  1. Linlin Li
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Correspondence to Xianpeng Wang.

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Li, L., Wang, X. An adaptive multiobjective evolutionary algorithm based on grid subspaces. Memetic Comp. 13, 249–269 (2021). https://doi.org/10.1007/s12293-021-00336-7

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